Tongliang Liu

ORCID

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 436

Top Venues

Publications

Jiyang Zheng, Yu Yao, Bo Han, Dadong Wang, Tongliang Liu
Enhancing Contrastive Learning for Ordinal Regression via Ordinal Content Preserved Data Augmentation. ICLR (2024)
Xue Dong, Xuemeng Song, Tongliang Liu, Weili Guan
Prompt-based Multi-interest Learning Method for Sequential Recommendation. CoRR (2024)
Suqin Yuan, Lei Feng, Tongliang Liu
Early Stopping Against Label Noise Without Validation Data. ICLR (2024)
Cong Lei, Yuxuan Du, Peng Mi, Jun Yu, Tongliang Liu
Neural Auto-designer for Enhanced Quantum Kernels. ICLR (2024)
Yunshui Li, Binyuan Hui, Xiaobo Xia, Jiaxi Yang, Min Yang, Lei Zhang, Shuzheng Si, Ling-Hao Chen, Junhao Liu, Tongliang Liu, Fei Huang, Yongbin Li
One-Shot Learning as Instruction Data Prospector for Large Language Models. ACL (1) (2024)
Jiacheng Zhang, Feng Liu, Dawei Zhou, Jingfeng Zhang, Tongliang Liu
Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training. CoRR (2024)
Xiaoli Wei, Zhaoqing Wang, Yandong Guo, Chunxia Zhang, Tongliang Liu, Mingming Gong
Training-Free Robust Interactive Video Object Segmentation. CoRR (2024)
Rundong He, Yue Yuan, Zhongyi Han, Fan Wang, Wan Su, Yilong Yin, Tongliang Liu, Yongshun Gong
Exploring Channel-Aware Typical Features for Out-of-Distribution Detection. AAAI (2024)
Zhuo Li, He Zhao, Zhen Li, Tongliang Liu, Dandan Guo, Xiang Wan
Extracting Clean and Balanced Subset for Noisy Long-tailed Classification. CoRR (2024)
Tianfu Wang, Li Shen, Qilin Fan, Tong Xu, Tongliang Liu, Hui Xiong
Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning. IEEE Trans. Serv. Comput. 17 (3) (2024)
Zhengning Wu, Tianyu He, Xiaobo Xia, Jun Yu, Xu Shen, Tongliang Liu
Conditional Consistency Regularization for Semi-Supervised Multi-Label Image Classification. IEEE Trans. Multim. 26 (2024)
Jialiang Shen, Yu Yao, Shaoli Huang, Zhiyong Wang, Jing Zhang, Ruxing Wang, Jun Yu, Tongliang Liu
ProtoSimi: label correction for fine-grained visual categorization. Mach. Learn. 113 (4) (2024)
Jingyi Wang, Xiaobo Xia, Long Lan, Xinghao Wu, Jun Yu, Wenjing Yang, Bo Han, Tongliang Liu
Tackling Noisy Labels With Network Parameter Additive Decomposition. IEEE Trans. Pattern Anal. Mach. Intell. 46 (9) (2024)
Qiang Qu, Yiran Shen, Xiaoming Chen, Yuk Ying Chung, Tongliang Liu
E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning. AAAI (2024)
Yonggang Zhang, Zhiqin Yang, Xinmei Tian, Nannan Wang, Tongliang Liu, Bo Han
Robust Training of Federated Models with Extremely Label Deficiency. CoRR (2024)
Hongduan Tian, Feng Liu, Tongliang Liu, Bo Du, Yiu-ming Cheung, Bo Han
MOKD: Cross-domain Finetuning for Few-shot Classification via Maximizing Optimized Kernel Dependence. CoRR (2024)
Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han
Negative Label Guided OOD Detection with Pretrained Vision-Language Models. CoRR (2024)
Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han
Negative Label Guided OOD Detection with Pretrained Vision-Language Models. ICLR (2024)
Longkang Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen, Tongliang Liu, Bin Gu, Kun Zhang
Federated Causal Discovery from Heterogeneous Data. ICLR (2024)
Wankou Yang, Jiren Mai, Fei Zhang, Tongliang Liu, Bo Han
Exploit CAM by itself: Complementary Learning System for Weakly Supervised Semantic Segmentation. Trans. Mach. Learn. Res. 2024 (2024)
Hongwei Bran Li, Fernando Navarro, Ivan Ezhov, Amirhossein Bayat, Dhritiman Das, Florian Kofler, Suprosanna Shit, Diana Waldmannstetter, Johannes C. Paetzold, Xiaobin Hu, Benedikt Wiestler, Lucas Zimmer, Tamaz Amiranashvili, Chinmay Prabhakar, Christoph Berger, Jonas Weidner, Michelle Alonso-Basanta, Arif Rashid, Ujjwal Baid, Wesam Adel, Deniz Alis, Bhakti Baheti, Yingbin Bai, Ishaan Bhat, Sabri Can Cetindag, Wenting Chen, Li Cheng, Prasad Dutande, Lara Dular, Mustafa A. Elattar, Ming Feng, Shengbo Gao, Henkjan Huisman, Weifeng Hu, Shubham Innani, Wei Jiat, Davood Karimi, Hugo J. Kuijf, Jin Tae Kwak, Hoang Long Le, Xiang Lia, Huiyan Lin, Tongliang Liu, Jun Ma, Kai Ma, Ting Ma, Ilkay Öksüz, Robbie Holland, Arlindo L. Oliveira, Jimut Bahan Pal, Xuan Pei, Maoying Qiao, Anindo Saha, Raghavendra Selvan, Linlin Shen, João Lourenço Silva, Ziga Spiclin, Sanjay N. Talbar, Dadong Wang, Wei Wang, Xiong Wang, Yin Wang, Ruiling Xia, Kele Xu, Yanwu Yan, Mert Yergin, Shuang Yu, Lingxi Zeng, YingLin Zhang, Jiachen Zhao, Yefeng Zheng, Martin Zukovec, Richard K. G. Do, Anton S. Becker, Amber L. Simpson, Ender Konukoglu, András Jakab, Spyridon Bakas, Leo Joskowicz, Bjoern H. Menze
QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge. CoRR (2024)
Rong Dai, Yonggang Zhang, Ang Li, Tongliang Liu, Xun Yang, Bo Han
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting. CoRR (2024)
Runqi Lin, Chaojian Yu, Bo Han, Tongliang Liu
On the Over-Memorization During Natural, Robust and Catastrophic Overfitting. ICLR (2024)
Qizhou Wang, Bo Han, Puning Yang, Jianing Zhu, Tongliang Liu, Masashi Sugiyama
Unlearning with Control: Assessing Real-world Utility for Large Language Model Unlearning. CoRR (2024)
Rong Dai, Yonggang Zhang, Ang Li, Tongliang Liu, Xun Yang, Bo Han
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting. ICLR (2024)
Zhenheng Tang, Yonggang Zhang, Shaohuai Shi, Xinmei Tian, Tongliang Liu, Bo Han, Xiaowen Chu
FedImpro: Measuring and Improving Client Update in Federated Learning. CoRR (2024)
Yulong Yang, Chenhao Lin, Qian Li, Zhengyu Zhao, Haoran Fan, Dawei Zhou, Nannan Wang, Tongliang Liu, Chao Shen
Quantization Aware Attack: Enhancing Transferable Adversarial Attacks by Model Quantization. IEEE Trans. Inf. Forensics Secur. 19 (2024)
Jun Nie, Yonggang Zhang, Zhen Fang, Tongliang Liu, Bo Han, Xinmei Tian
Out-of-Distribution Detection with Negative Prompts. ICLR (2024)
DeLiang Wang, Mauro Forti, Tongliang Liu, Taro Toyoizumi
Expansion of the editorial team. Neural Networks 173 (2024)
Yonggang Zhang, Zhiqin Yang, Xinmei Tian, Nannan Wang, Tongliang Liu, Bo Han
Robust Training of Federated Models with Extremely Label Deficiency. ICLR (2024)
Pengfei Zheng, Yonggang Zhang, Zhen Fang, Tongliang Liu, Defu Lian, Bo Han
NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation. ICLR (2024)
Xiaobo Xia, Pengqian Lu, Chen Gong, Bo Han, Jun Yu, Jun Yu, Tongliang Liu
Regularly Truncated M-Estimators for Learning With Noisy Labels. IEEE Trans. Pattern Anal. Mach. Intell. 46 (5) (2024)
Loka Li, Ignavier Ng, Gongxu Luo, Biwei Huang, Guangyi Chen, Tongliang Liu, Bin Gu, Kun Zhang
Federated Causal Discovery from Heterogeneous Data. CoRR (2024)
Runqi Lin, Chaojian Yu, Bo Han, Hang Su, Tongliang Liu
Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency. CoRR (2024)
Cong Lei, Yuxuan Du, Peng Mi, Jun Yu, Tongliang Liu
Neural auto-designer for enhanced quantum kernels. CoRR (2024)
Chentao Cao, Zhun Zhong, Zhanke Zhou, Yang Liu, Tongliang Liu, Bo Han
Envisioning Outlier Exposure by Large Language Models for Out-of-Distribution Detection. CoRR (2024)
Qiang Qu, Yiran Shen, Xiaoming Chen, Yuk Ying Chung, Tongliang Liu
E2HQV: High-Quality Video Generation from Event Camera via Theory-Inspired Model-Aided Deep Learning. CoRR (2024)
Runqi Lin, Chaojian Yu, Tongliang Liu
Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization. CoRR (2024)
Xiu-Chuan Li, Kun Zhang, Tongliang Liu
Causal Structure Recovery with Latent Variables under Milder Distributional and Graphical Assumptions. ICLR (2024)
Yuhao Wu, Jiangchao Yao, Bo Han, Lina Yao, Tongliang Liu
Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning. CoRR (2024)
Shaokun Zhang, Xiaobo Xia, Zhaoqing Wang, Ling-Hao Chen, Jiale Liu, Qingyun Wu, Tongliang Liu
IDEAL: Influence-Driven Selective Annotations Empower In-Context Learners in Large Language Models. ICLR (2024)
Tianfu Wang, Li Shen, Qilin Fan, Tong Xu, Tongliang Liu, Hui Xiong
Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning. CoRR (2024)
Chenxi Liu, Yongqiang Chen, Tongliang Liu, Mingming Gong, James Cheng, Bo Han, Kun Zhang
Discovery of the Hidden World with Large Language Models. CoRR (2024)
Zhenheng Tang, Yonggang Zhang, Shaohuai Shi, Xinmei Tian, Tongliang Liu, Bo Han, Xiaowen Chu
FedImpro: Measuring and Improving Client Update in Federated Learning. ICLR (2024)
Zhaoqing Wang, Xiaobo Xia, Ziye Chen, Xiao He, Yandong Guo, Mingming Gong, Tongliang Liu
Open-Vocabulary Segmentation with Unpaired Mask-Text Supervision. CoRR (2024)
Yiwei Zhou, Xiaobo Xia, Zhiwei Lin, Bo Han, Tongliang Liu
Few-Shot Adversarial Prompt Learning on Vision-Language Models. CoRR (2024)
Jingyi Wang, Xiaobo Xia, Long Lan, Xinghao Wu, Jun Yu, Wenjing Yang, Bo Han, Tongliang Liu
Tackling Noisy Labels with Network Parameter Additive Decomposition. CoRR (2024)
Run Luo, Yunshui Li, Longze Chen, Wanwei He, Ting-En Lin, Ziqiang Liu, Lei Zhang, Zikai Song, Xiaobo Xia, Tongliang Liu, Min Yang, Binyuan Hui
DEEM: Diffusion Models Serve as the Eyes of Large Language Models for Image Perception. CoRR (2024)
Ziming Hong, Zhenyi Wang, Li Shen, Yu Yao, Zhuo Huang, Shiming Chen, Chuanwu Yang, Mingming Gong, Tongliang Liu
Improving Non-Transferable Representation Learning by Harnessing Content and Style. ICLR (2024)
Yuhao Wu, Jiangchao Yao, Xiaobo Xia, Jun Yu, Ruxin Wang, Bo Han, Tongliang Liu
Mitigating Label Noise on Graph via Topological Sample Selection. CoRR (2024)
Yuxiang Zheng, Zhongyi Han, Yilong Yin, Xin Gao, Tongliang Liu
Can We Treat Noisy Labels as Accurate? CoRR (2024)
Jingfeng Zhang, Bo Song, Haohan Wang, Bo Han, Tongliang Liu, Lei Liu, Masashi Sugiyama
BadLabel: A Robust Perspective on Evaluating and Enhancing Label-Noise Learning. IEEE Trans. Pattern Anal. Mach. Intell. 46 (6) (2024)
Pengfei Zheng, Yonggang Zhang, Zhen Fang, Tongliang Liu, Defu Lian, Bo Han
NoiseDiffusion: Correcting Noise for Image Interpolation with Diffusion Models beyond Spherical Linear Interpolation. CoRR (2024)
Guanglin Zhou, Zhongyi Han, Shiming Chen, Biwei Huang, Liming Zhu, Tongliang Liu, Lina Yao, Kun Zhang
HCVP: Leveraging Hierarchical Contrastive Visual Prompt for Domain Generalization. CoRR (2024)
Wenjie Wang, Biwei Huang, Feng Liu, Xinge You, Tongliang Liu, Kun Zhang, Mingming Gong
Optimal Kernel Choice for Score Function-based Causal Discovery. CoRR (2024)
Songhua Wu, Tianyi Zhou, Yuxuan Du, Jun Yu, Bo Han, Tongliang Liu
A Time-Consistency Curriculum for Learning From Instance-Dependent Noisy Labels. IEEE Trans. Pattern Anal. Mach. Intell. 46 (7) (2024)
Mingyu Li, Tao Zhou, Bo Han, Tongliang Liu, Xinkai Liang, Jiajia Zhao, Chen Gong
Class-Wise Contrastive Prototype Learning for Semi-Supervised Classification Under Intersectional Class Mismatch. IEEE Trans. Multim. 26 (2024)
Chuang Liu, Yibing Zhan, Jia Wu, Chang Li, Bo Du, Wenbin Hu, Tongliang Liu, Dacheng Tao
Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities. IJCAI (2023)
Muyang Li, Runze Wu, Haoyu Liu, Jun Yu, Xun Yang, Bo Han, Tongliang Liu
InstanT: Semi-supervised Learning with Instance-dependent Thresholds. CoRR (2023)
Jiaheng Wei, Zhaowei Zhu, Gang Niu, Tongliang Liu, Sijia Liu, Masashi Sugiyama, Yang Liu
Fairness Improves Learning from Noisily Labeled Long-Tailed Data. CoRR (2023)
Shuxian Liang, Xu Shen, Tongliang Liu, Xian-Sheng Hua
Contextual Convolutional Networks. ICLR (2023)
Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation. CoRR (2023)
Jinkai Tian, Xiaoyu Sun, Yuxuan Du, Shanshan Zhao, Qing Liu, Kaining Zhang, Wei Yi, Wanrong Huang, Chaoyue Wang, Xingyao Wu, Min-Hsiu Hsieh, Tongliang Liu, Wenjing Yang, Dacheng Tao
Recent Advances for Quantum Neural Networks in Generative Learning. IEEE Trans. Pattern Anal. Mach. Intell. 45 (10) (2023)
Maoyuan Ye, Jing Zhang, Shanshan Zhao, Juhua Liu, Tongliang Liu, Bo Du, Dacheng Tao
DeepSolo: Let Transformer Decoder with Explicit Points Solo for Text Spotting. CVPR (2023)
Xiaobo Xia, Jiankang Deng, Wei Bao, Yuxuan Du, Bo Han, Shiguang Shan, Tongliang Liu
Holistic Label Correction for Noisy Multi-Label Classification. ICCV (2023)
Runnan Chen, Youquan Liu, Lingdong Kong, Nenglun Chen, Xinge Zhu, Yuexin Ma, Tongliang Liu, Wenping Wang
Towards Label-free Scene Understanding by Vision Foundation Models. NeurIPS (2023)
Shuo Yang, Zhaopan Xu, Kai Wang, Yang You, Hongxun Yao, Tongliang Liu, Min Xu
BiCro: Noisy Correspondence Rectification for Multi-modality Data via Bi-directional Cross-modal Similarity Consistency. CoRR (2023)
Haotian Zheng, Qizhou Wang, Zhen Fang, Xiaobo Xia, Feng Liu, Tongliang Liu, Bo Han
Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources. CoRR (2023)
Xiang An, Jiankang Deng, Kaicheng Yang, Jaiwei Li, Ziyong Feng, Jia Guo, Jing Yang, Tongliang Liu
Unicom: Universal and Compact Representation Learning for Image Retrieval. ICLR (2023)
Suqin Yuan, Lei Feng, Tongliang Liu
Late Stopping: Avoiding Confidently Learning from Mislabeled Examples. CoRR (2023)
Zhiqin Yang, Yonggang Zhang, Yu Zheng, Xinmei Tian, Hao Peng, Tongliang Liu, Bo Han
FedFed: Feature Distillation against Data Heterogeneity in Federated Learning. CoRR (2023)
Ling-Hao Chen, Yuanshuo Zhang, Taohua Huang, Liangcai Su, Zeyi Lin, Xi Xiao, Xiaobo Xia, Tongliang Liu
ERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance. CoRR (2023)
Xiu-Chuan Li, Xiaobo Xia, Fei Zhu, Tongliang Liu, Xu-Yao Zhang, Cheng-Lin Liu
Dynamics-aware loss for learning with label noise. Pattern Recognit. 144 (2023)
Vinoth Nandakumar, Arush Tagade, Tongliang Liu
Why do CNNs excel at feature extraction? A mathematical explanation. CoRR (2023)
Haotian Zheng, Qizhou Wang, Zhen Fang, Xiaobo Xia, Feng Liu, Tongliang Liu, Bo Han
Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources. NeurIPS (2023)
Zhuo Huang, Chang Liu, Yinpeng Dong, Hang Su, Shibao Zheng, Tongliang Liu
Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning. CoRR (2023)
Yu Yao, Baosheng Yu, Chen Gong, Tongliang Liu
Understanding How Pretraining Regularizes Deep Learning Algorithms. IEEE Trans. Neural Networks Learn. Syst. 34 (9) (2023)
Jianing Zhu, Jiangchao Yao, Tongliang Liu, Quanming Yao, Jianliang Xu, Bo Han
Combating Exacerbated Heterogeneity for Robust Models in Federated Learning. CoRR (2023)
Qizhou Wang, Junjie Ye, Feng Liu, Quanyu Dai, Marcus Kalander, Tongliang Liu, Jianye Hao, Bo Han
Out-of-distribution Detection with Implicit Outlier Transformation. ICLR (2023)
Shuo Yang, Zhaopan Xu, Kai Wang, Yang You, Hongxun Yao, Tongliang Liu, Min Xu
BiCro: Noisy Correspondence Rectification for Multi-modality Data via Bi-directional Cross-modal Similarity Consistency. CVPR (2023)
Zhuo Huang, Xiaobo Xia, Li Shen, Bo Han, Mingming Gong, Chen Gong, Tongliang Liu
Harnessing Out-Of-Distribution Examples via Augmenting Content and Style. ICLR (2023)
Shikun Li, Xiaobo Xia, Hansong Zhang, Shiming Ge, Tongliang Liu
Multi-Label Noise Transition Matrix Estimation with Label Correlations: Theory and Algorithm. CoRR (2023)
Dawei Zhou, Nannan Wang, Heng Yang, Xinbo Gao, Tongliang Liu
Phase-aware Adversarial Defense for Improving Adversarial Robustness. ICML (2023)
Jianing Zhu, Yu Geng, Jiangchao Yao, Tongliang Liu, Gang Niu, Masashi Sugiyama, Bo Han
Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation. NeurIPS (2023)
Hui Kang, Sheng Liu, Huaxi Huang, Jun Yu, Bo Han, Dadong Wang, Tongliang Liu
Unleashing the Potential of Regularization Strategies in Learning with Noisy Labels. CoRR (2023)
Jingwei Zhang, Tongliang Liu, Dacheng Tao
An Optimal Transport Analysis on Generalization in Deep Learning. IEEE Trans. Neural Networks Learn. Syst. 34 (6) (2023)
Jianing Zhu, Jiangchao Yao, Tongliang Liu, Quanming Yao, Jianliang Xu, Bo Han
Combating Exacerbated Heterogeneity for Robust Models in Federated Learning. ICLR (2023)
Xiu-Chuan Li, Xiaobo Xia, Fei Zhu, Tongliang Liu, Xu-Yao Zhang, Cheng-Lin Liu
Dynamic-Aware Loss for Learning with Label Noise. CoRR (2023)
Zixuan Hu, Li Shen, Zhenyi Wang, Tongliang Liu, Chun Yuan, Dacheng Tao
Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning. CoRR (2023)
Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han
Detecting Out-of-distribution Data through In-distribution Class Prior. ICML (2023)
Chengxin Liu, Hao Lu, Zhiguo Cao, Tongliang Liu
Point-Query Quadtree for Crowd Counting, Localization, and More. CoRR (2023)
Jingfeng Zhang, Bo Song, Haohan Wang, Bo Han, Tongliang Liu, Lei Liu, Masashi Sugiyama
BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning. CoRR (2023)
Ruijiang Dong, Feng Liu, Haoang Chi, Tongliang Liu, Mingming Gong, Gang Niu, Masashi Sugiyama, Bo Han
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation. ICML (2023)
Shiming Chen, Wenjin Hou, Ziming Hong, Xiaohan Ding, Yibing Song, Xinge You, Tongliang Liu, Kun Zhang
Evolving Semantic Prototype Improves Generative Zero-Shot Learning. ICML (2023)
Zhuo Huang, Li Shen, Jun Yu, Bo Han, Tongliang Liu
FlatMatch: Bridging Labeled Data and Unlabeled Data with Cross-Sharpness for Semi-Supervised Learning. CoRR (2023)
Xinbiao Wang, Junyu Liu, Tongliang Liu, Yong Luo, Yuxuan Du, Dacheng Tao
Symmetric Pruning in Quantum Neural Networks. ICLR (2023)
Shikun Li, Tongliang Liu, Jiyong Tan, Dan Zeng, Shiming Ge
Trustable Co-Label Learning From Multiple Noisy Annotators. IEEE Trans. Multim. 25 (2023)
Huaxi Huang, Hui Kang, Sheng Liu, Olivier Salvado, Thierry Rakotoarivelo, Dadong Wang, Tongliang Liu
PADDLES: Phase-Amplitude Spectrum Disentangled Early Stopping for Learning with Noisy Labels. ICCV (2023)
Xuefeng Du, Tian Bian, Yu Rong, Bo Han, Tongliang Liu, Tingyang Xu, Wenbing Huang, Yixuan Li, Junzhou Huang
Noise-robust Graph Learning by Estimating and Leveraging Pairwise Interactions. Trans. Mach. Learn. Res. 2023 (2023)
Chenhong Zhou, Feng Liu, Chen Gong, Rongfei Zeng, Tongliang Liu, Kwok-Wai Cheung, Bo Han
KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation. Trans. Mach. Learn. Res. 2023 (2023)

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Publication Activity (10 Years)

Top Topics

Top Venues

Tongliang Liu